



'''simple docstring'''



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
							from PIL import Image
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Optional[int]						  =					(
						    'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
						    'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
						    'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
						)
						_lowerCAmelCase  : int						  =					'CIDAS/clipseg-rd64-refined'
						_lowerCAmelCase  : Dict						  =					'image_segmenter'
						_lowerCAmelCase  : Any						  =					CLIPSegForImageSegmentation

						_lowerCAmelCase  : Optional[Any]						  =					['image', 'text']
						_lowerCAmelCase  : Optional[Any]						  =					['image']







						def __init__(	self :   List[Any]					,     *lowerCAmelCase__ :   Tuple					,     **lowerCAmelCase__ :   int					):

													"""simple docstring"""
													requires_backends(self					,     ['''vision''']					)
													super().__init__(*lowerCAmelCase__					,     **lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   "Image"					,     lowerCAmelCase__ :   str					):

													"""simple docstring"""
													return self.pre_processor(text=[label]					,     images=[image]					,     padding=lowerCAmelCase__					,     return_tensors='''pt'''					)







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   str					):

													"""simple docstring"""
													with torch.no_grad():
																				SCREAMING_SNAKE_CASE       :   int         =      self.model(**lowerCAmelCase__					).logits
													return logits







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      outputs.cpu().detach().numpy()
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      1
													return Image.fromarray((array * 2_55).astype(np.uinta					)					)









'''simple docstring'''



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							List[Any]             =    logging.get_logger(__name__)
def        UpperCAmelCase (    A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      torch.load(A       ,		map_location='''cpu'''					)
							if "model" in sd.keys():
														SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.load(A       ,		map_location='''cpu'''					)['''model''']

							# pop unnecessary weights
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      [
							    '''decoder.version''',
							    '''decoder.output_projection.weight''',
							]
							for key in keys_to_delete:
														if key in sd:
																					sd.pop(A					)

							SCREAMING_SNAKE_CASE       :   int         =      {
							    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
							    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
							    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
							    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
							}
							for old_key, new_key in keys_to_rename.items():
														if old_key in sd:
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      sd.pop(A					)

							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      list(sd.keys()					)
							for key in keys:
														if ".qkv_proj." in key:
																					SCREAMING_SNAKE_CASE       :   str         =      sd[key]
																					# We split QKV in separate Q,K,V

																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      key.replace('''.qkv_proj.'''       ,		'''.q_proj.'''					)
																					SCREAMING_SNAKE_CASE       :   str         =      key.replace('''.qkv_proj.'''       ,		'''.k_proj.'''					)
																					SCREAMING_SNAKE_CASE       :   Any         =      key.replace('''.qkv_proj.'''       ,		'''.v_proj.'''					)

																					SCREAMING_SNAKE_CASE       :   Tuple         =      value.shape[0]
																					assert depth % 3 == 0
																					# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
																					# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
																					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Tuple         =      torch.split(A       ,		depth // 3       ,		dim=0					)

																					SCREAMING_SNAKE_CASE       :   List[str]         =      q
																					SCREAMING_SNAKE_CASE       :   List[str]         =      k
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      v
																					del sd[key]

							return sd
@torch.no_grad()
def        UpperCAmelCase (    A	:					int       ,		A	:					Union[str, Any]       ,		A	:					Dict=None					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_checkpoint(A					)

							if config is not None:
														SCREAMING_SNAKE_CASE       :   Tuple         =      OPTConfig.from_pretrained(A					)
							else:
														SCREAMING_SNAKE_CASE       :   int         =      OPTConfig()

							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      OPTModel(A					).half().eval()
							model.load_state_dict(A					)

							# Check results
							Path(A					).mkdir(exist_ok=A					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							List[str]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--fairseq_path',
							    type=str,
							    help=(
							        'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
							        ' https://huggingface.co/models?other=opt_metasq'
							    ),
							)
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
							lowerCAmelCase_  :							str             =    parser.parse_args()
							convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)




